After the identification of unknowns, an important requisite for an open world detector is to be able to learn new classes, when the labeled examples of some of the unknown classes of interest are provided.
Importantly, the training data for the previous tasks will not be present at this stage since retraining from scratch is not a feasible solution. Training with only the new class instances will lead to catastrophic forgetting [1]}, [2]} of the previous classes.
We note that a number of involved approaches have been developed to alleviate such forgetting, including methods based on parameter regularization [3]}, [4]}, [5]}, [6]}, exemplar replay [7]}, [8]}, [9]}, [10]}, dynamically expanding networks [11]}, [12]}, [13]} and meta-learning [14]}, [15]}.
